1.School of Geographical Science,Nanjing University of Information Science Technology;2.State Key Laboratory of Soil and Sustainable Agriculture,Institute of Soil Science,Chinese Academy of Sciences;3.Upland Flue-cured Tobacco Quality Ecology Key Laboratory of China Tobacco,Guizhou Academy of Tobacco Science;4.China National Tobacco Corporation Guizhou Provincial Company
The National Natural Science Foundation of China (No.42107322), China Tobacco Corporation Guizhou Provincial Company Science and Technology Project (No.201910), and Natural Science Foundation of colleges and universities of Jiangsu Province (No.20KJB210009)
对土壤养分的快速和准确测定有助于适时指导施肥。为进一步研究可见-近红外（350～2 500 nm）与中红外光谱（4 000～650 cm-1）对土壤养分的预测能力，本研究以贵州省500个土样为例，对光谱进行Savitzky-Golay（SG）平滑去噪处理，再用标准正态化（SNV）方法进行基线校正，然后分别应用偏最小二乘回归（PLSR）和支持向量机（SVM）两种方法进行建模，探讨了可见-近红外和中红外光谱对土壤全氮（TN）、全磷（TP）、全钾（TK）和碱解氮（AN）、有效磷（AP）、速效钾（AK）共六种土壤养分的预测效果。结果发现：（1）无论基于可见-近红外光谱还是中红外光谱，PLSR模型的预测精度整体均优于SVM模型。（2）中红外光谱对TN、TK和AN的预测精度均显著高于可见-近红外光谱，可见-近红外和中红外光谱均可以可靠地预测TN和TK（性能与四分位间隔距离的比率（RPIQ）>2.10），中红外光谱可相对较可靠地预测AN（RPIQ=1.87）；但两类光谱对TP、AP和AK的预测效果均较差（RPIQ<1.34）。（3）当变量投影重要性得分（VIP）大于1.5时，PLSR模型在中红外光谱区域预测TN和TK的重要波段多于可见-近红外光谱区域，TN的重要波段主要集中于可见-近红外光谱区域的1 910和2 207 nm附近，中红外光谱区域的1 120、1 000、960、910、770和668 cm-1附近；TK的重要波段主要集中于可见-近红外光谱区域的540、2 176、2 225和2 268 nm附近，中红外光谱区域的1 040、960、910、776、720和668 cm-1附近。因此，中红外光谱技术结合PLSR模型对土壤养分预测效果较好，可快速准确预测土壤TN和TK，可为指导适时施肥提供技术支撑。
【Objective】Predicting soil nutrients by visible-near infrared (vis-NIR) and mid-infrared (MIR) spectroscopy has the advantages of being fast, cost-effective and environmental friendly. Soil spectra contain abundant information of soil properties, and can be combined with machine learning methods to effectively and accurately predict soil nutrients, which can provide support and guidance for timely fertilization management. The objective of this study was to compare the predictive ability of vis-NIR (350-2 500 nm) and MIR spectroscopy (4 000-650 cm-1) for predicting both the total and available contents of soil nitrogen (N), phosphorus (P) and potassium (K), in order to construct an optimal model for estimation of different nutrient contents. 【Method】In this study, 500 samples were collected from the surface layers (0-20 cm) of the dryland in Guizhou Province for determination of soil N, P and K contents and spectral analysis. The vis-NIR spectra were measured by Cary 5000 and the MIR spectra by Thermo Scientifit Nicolet iS50. Soil spectra were pre-processed by Savitzky-Golay (SG) smoothing for denoising and standard normal variate (SNV) transformation for baseline correction. Partial least squares regression (PLSR) and support vector machine (SVM) were used to predict the contents of total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali-hydrolyzable nitrogen (AN), available phosphorus (AP) and available potassium (AK). 【Result】The results showed that: (1) Whether using the vis-NIR spectroscopy or the MIR spectroscopy, the prediction accuracy of PLSR model was better than that of SVM model. (2) The accuracy of MIR spectroscopy for prediction of TN, TK and AN was significantly higher than that of vis-NIR spectroscopy. Vis-NIR and MIR spectroscopy could reliably predict TN and TK (ratio of performance to interquartile distance (RPIQ) > 2.10), while MIR spectroscopy could predict AN with moderate accuracy (RPIQ = 1.87). However, both types of spectra had poor ability to predict TP, AP and AK (RPIQ < 1.34). (3) When the variable in the projection (VIP) score was > 1.5, there were more important bands selected by PLSR models in the MIR region than the vis-NIR region. The important bands selected for estimation of TN were mainly concentrated near 1 910 and 2 207 nm in the vis-NIR region, and centered around 1 120, 1 000, 960, 910, 770, and 668 cm-1 in the MIR region. The important bands of TK were mainly distributed around 540, 2 176, 2 225, and 2 268 nm in the vis-NIR region, and around 1 040, 960, 910, 776, 720, and 668 cm-1 in the MIR region. 【Conclusion】Therefore, MIR spectroscopy combined with PLSR model proved to be promising for accurate prediction of soil nutrients, especially for the estimation of TN and TK, and can provide technical support for guiding timely fertilization.
LI Xuelan, LI Decheng, ZHENG Guanghui, ZENG Rong, CAI Kai, GAO Weichang, PAN Wenjie, JIANG Chaoying, ZENG Yuntao. Comparative Study on Prediction of Soil Nutrients by Visible-Near Infrared and Mid-infrared Spectroscopy[J]. Acta Pedologica Sinica,,[In Press]